Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell


With the prevalence of machine-generated real-time data, including but not limited to IoT sensors, devices, and beacons, it is increasingly important to gain insight into this fire hose of data as quickly as it is being created. Whether you are detecting fraudulent transactions, real-time detection of sensor anomalies, or sentiment analysis of the next cat video, streaming analytics is an increasingly important differentiator and business advantage.

As we progress through these recipes, we will be combining the constructs of batch and real-time processing for the creation of continuous applications. With Apache Spark, data scientists and data engineers can analyze their data using Spark SQL in batch and in real time, train machine learning models with MLlib, and score these models via Spark Streaming.

An important reason for the rapid adoption of Apache Spark is that it unifies all of these disparate data processing paradigms (machine learning via ML and MLlib, Spark SQL, and...